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The complexity inherent in optimization problems arises from a combination of interacting factors, including multiple local optima, irregular search landscapes, and symmetrical patterns that influence scaling sensitivity. In addition, interdependent design variables and intricate basin structures often create misleading search cues, further complicating the optimization process. To enhance performance and mitigate these challenges, the Special Relativity Search (SRS) algorithm is integrated with Differential Evolution (DE). This hybridization leads to faster convergence and improved solution quality. The SRS algorithm draws its conceptual foundation from Einstein\u2019s theory of special relativity by emulating the motion of charged particles within a magnetic field. Within this framework, the search space is modeled as a magnetic field, and the candidate solutions behave as charged particles subjected solely to the magnetic component of the Lorentz force. The incorporation of dynamic guidance significantly enhances search efficiency, rendering the method suitable for complex engineering applications such as structural design and control optimization. The proposed approach was evaluated on two structurally complex optimization problems involving both continuous and discrete design variables. Performance comparisons among algorithms were conducted through statistical analyses. 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